Visualize integration (level 3): Cytotoxic
3 junio 2021
1 Introduction
The objectives of this notebook are:
Visualize how well we could remove technical variability associated with the assay (scATAC or multiome). We should see a high degree of intermixing between the 2 assays.
Plot several markers of doublets: scRNAseq doublets determined by multiome, scrublet predictions, number of features, Chromatin Signature, etc. The broader objective of level 2 is to eliminate most remaining doublets and poor-quality cells, as we will discuss in future notebooks. These visualizations will allow us to explore this. We will also plot their location in the UMAP obtained at level 1.
2 Pre-processing
2.1 Load packages
library(Seurat)
library(tidyverse)
library(reshape2)
library(ggpubr)2.2 Parameters
# Paths
path_to_obj <- str_c(
here::here("scATAC-seq/results/R_objects/level_3/"),
params$cell_type,
"/",
params$cell_type,
"_integrated_level_3.rds",
sep = ""
)
path_to_obj_RNA <- str_c(
here::here("scRNA-seq/3-clustering/3-level_3/"),
params$cell_type,
"_clustered_level_3_with_pre_freeze.rds",
sep = ""
)
path_to_doublets <- here::here("scRNA-seq/3-clustering/2-level_2/tmp/doublets_multiome_df_all.rds")
# Functions
source(here::here("scRNA-seq/bin/utils.R"))
# Colors
color_palette <- c("black", "gray", "red", "yellow", "violet", "green4",
"blue", "chocolate1", "coral2", "blueviolet",
"brown1", "darkmagenta", "deepskyblue1", "dimgray",
"deeppink1", "green", "lightgray", "hotpink1",
"indianred4", "khaki", "mediumorchid2", "gold", "gray")2.3 Load data
# Seurat object
seurat <- readRDS(path_to_obj)
seurat## An object of class Seurat
## 270784 features across 3395 samples within 1 assay
## Active assay: peaks_macs (270784 features, 107717 variable features)
## 3 dimensional reductions calculated: umap, lsi, harmonyseurat_RNA <- readRDS(path_to_obj_RNA)
seurat_RNA## An object of class Seurat
## 37378 features across 10998 samples within 1 assay
## Active assay: RNA (37378 features, 0 variable features)
## 3 dimensional reductions calculated: pca, umap, harmonytonsil_RNA_annotation <- seurat_RNA@meta.data %>%
rownames_to_column(var = "cell_barcode") %>%
dplyr::filter(assay == "multiome") %>%
dplyr::select("cell_barcode", "seurat_clusters")
colnames(tonsil_RNA_annotation) <- c("cell_barcode", "seurat_clusters_RNA")2.3.1 Visualization of the data
p1 <- DimPlot(seurat,
pt.size = 0.1)
p2 <- DimPlot(seurat_RNA,
group.by = "seurat_clusters",
pt.size = 0.1,cols = color_palette)
p1
p2
3 Assess integration
3.1 Removal of technical variability
p_assay <- plot_split_umap(seurat, var = "assay")
p_assay
4 Spot potential doublets
4.1 Multiome scRNA-seq data
4.1.1 Doublets detected in scRNA-seq data from Multiome.
Here, we can see the count of doublets detected by cell-type. Note that for the level1 annotation there are not doublets detected in the Naive and Memory B cell cluster.
multiome_doublets <- readRDS(path_to_doublets)
dfm = melt(table(multiome_doublets$cell_type))
dfm$value = as.numeric(as.character(dfm$value))
ggbarplot(dfm, x = "Var1", y = "value",
fill = "Var1",
palette = "jco",
sort.val = "desc",
sort.by.groups = FALSE,
x.text.angle = 90
)
doublets_cells <- colnames(seurat)[which(colnames(seurat) %in% multiome_doublets$barcode)]
length(doublets_cells)## [1] 04.2 Visualize the projection of the doublets cells onto the scATAC-seq UMAP
DimPlot(
seurat, reduction = "umap",
cols.highlight = "darkred", cols= "grey",
cells.highlight= doublets_cells,
pt.size = 0.1
)
## Scrublet prediction
# Scrublet
DimPlot(seurat, group.by = "scrublet_predicted_doublet_atac")
table(seurat$scrublet_predicted_doublet_atac)##
## FALSE TRUE
## 3167 2284.3 QC metrics
qc_vars <- c(
"nCount_peaks",
"nFeature_peaks",
"nucleosome_signal",
"TSS.enrichment"
)
qc_gg <- purrr::map(qc_vars, function(x) {
p <- FeaturePlot(seurat, features = x, max.cutoff = "q95")
p
})
qc_gg## [[1]]
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4.4 Annotation probability
qc_vars <- c(
"annotation_prob")
qc_gg <- purrr::map(qc_vars, function(x) {
p <- FeaturePlot(seurat, features = x)
p
})
qc_gg## [[1]]
4.5 Chromatin Signature
qc_vars <- c("NBC.MBC", "GCBC", "PC", "CD4.T", "Cytotoxic")
qc_gg <- purrr::map(qc_vars, function(x) {
p <- FeaturePlot(seurat, feature = x,
max.cutoff = 4, min.cutoff = -4) +
scale_color_viridis_c(option = "magma")
p
})
qc_gg## [[1]]
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4.6 Projection of the scRNA-seq clusters to scATAC-seq UMAP
Annotation level 3 for scATAC will be defined “a priori” as unannotated and the scRNA annotation will be transfered to the scATAC-multiome cells based on the same cell barcode.
cell_testing <- c("NBC_MBC", "GCBC", "PC", "CD4_T","Cytotoxic")
if (unique(seurat$annotation_level_1) %in% cell_testing){
seurat_df <- data.frame(cell_barcode = colnames(seurat)[seurat$assay == "scATAC"])
seurat_df$seurat_clusters_RNA <- "unannotated"
df_all <- rbind(tonsil_RNA_annotation,seurat_df)
rownames(df_all) <- df_all$cell_barcode
df_all <- df_all[colnames(seurat), ]
seurat$seurat_clusters_RNA <- df_all$seurat_clusters_RNA
seurat@meta.data$annotation_prob <- 1
melt(table(seurat$seurat_clusters_RNA))
table(is.na(seurat$seurat_clusters_RNA))
seurat_multiome <- subset(seurat, assay == "multiome")
p3 <- DimPlot(seurat_multiome, pt.size = 0.7, group.by = "seurat_clusters_RNA",cols = color_palette)
p3
}
p2 + p3
4.7 UMAP level 1
umap_level_1 <- FeatureScatter(
seurat,
"UMAP_1_level_1",
"UMAP_2_level_1",
group.by = "annotation_level_1"
)
umap_level_1 <- umap_level_1 +
theme(
#legend.position = "none",
plot.title = element_blank()
)
umap_level_1
5 Session Information
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS/LAPACK: /Users/pauli/opt/anaconda3/envs/Tonsil_atlas/lib/libopenblasp-r0.3.10.dylib
##
## locale:
## [1] es_ES.UTF-8/UTF-8/es_ES.UTF-8/C/es_ES.UTF-8/es_ES.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] Signac_1.1.0.9000 ggpubr_0.4.0 reshape2_1.4.4 forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4 readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 ggplot2_3.3.2 tidyverse_1.3.0 Seurat_3.9.9.9010 BiocStyle_2.16.1
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.1 SnowballC_0.7.0 rtracklayer_1.48.0 GGally_2.0.0 bit64_4.0.5 knitr_1.30 irlba_2.3.3 DelayedArray_0.14.0 data.table_1.13.2 rpart_4.1-15 RCurl_1.98-1.2 AnnotationFilter_1.12.0 generics_0.1.0 BiocGenerics_0.34.0 GenomicFeatures_1.40.1 cowplot_1.1.0 RSQLite_2.2.1 RANN_2.6.1 future_1.20.1 bit_4.0.4 spatstat.data_2.1-0 xml2_1.3.2 lubridate_1.7.9 httpuv_1.5.4 ggsci_2.9 SummarizedExperiment_1.18.1 assertthat_0.2.1 xfun_0.18 hms_0.5.3 evaluate_0.14 promises_1.1.1 fansi_0.4.1 progress_1.2.2 dbplyr_1.4.4 readxl_1.3.1 igraph_1.2.6 DBI_1.1.0 htmlwidgets_1.5.2 reshape_0.8.8 stats4_4.0.3 ellipsis_0.3.1 backports_1.2.0
## [43] bookdown_0.21 biomaRt_2.44.4 deldir_0.2-3 vctrs_0.3.4 Biobase_2.48.0 here_1.0.1 ensembldb_2.12.1 ROCR_1.0-11 abind_1.4-5 withr_2.3.0 ggforce_0.3.2 BSgenome_1.56.0 checkmate_2.0.0 sctransform_0.3.1 GenomicAlignments_1.24.0 prettyunits_1.1.1 goftest_1.2-2 cluster_2.1.0 lazyeval_0.2.2 crayon_1.3.4 labeling_0.4.2 pkgconfig_2.0.3 tweenr_1.0.1 GenomeInfoDb_1.24.0 nlme_3.1-150 ProtGenerics_1.20.0 nnet_7.3-14 rlang_0.4.11 globals_0.13.1 lifecycle_0.2.0 miniUI_0.1.1.1 BiocFileCache_1.12.1 modelr_0.1.8 rsvd_1.0.3 dichromat_2.0-0 cellranger_1.1.0 rprojroot_2.0.2 polyclip_1.10-0 matrixStats_0.57.0 lmtest_0.9-38 graph_1.66.0 ggseqlogo_0.1
## [85] Matrix_1.3-2 carData_3.0-4 zoo_1.8-8 reprex_0.3.0 base64enc_0.1-3 ggridges_0.5.2 png_0.1-7 viridisLite_0.3.0 bitops_1.0-6 KernSmooth_2.23-17 Biostrings_2.56.0 blob_1.2.1 parallelly_1.21.0 jpeg_0.1-8.1 rstatix_0.6.0 S4Vectors_0.26.0 ggsignif_0.6.0 scales_1.1.1 memoise_1.1.0 magrittr_1.5 plyr_1.8.6 ica_1.0-2 zlibbioc_1.34.0 compiler_4.0.3 RColorBrewer_1.1-2 fitdistrplus_1.1-1 Rsamtools_2.4.0 cli_2.1.0 XVector_0.28.0 listenv_0.8.0 patchwork_1.1.0 pbapply_1.4-3 ps_1.4.0 htmlTable_2.1.0 Formula_1.2-4 MASS_7.3-53 mgcv_1.8-33 tidyselect_1.1.0 stringi_1.5.3 yaml_2.2.1 askpass_1.1 latticeExtra_0.6-29
## [127] ggrepel_0.8.2 grid_4.0.3 VariantAnnotation_1.34.0 fastmatch_1.1-0 tools_4.0.3 future.apply_1.6.0 parallel_4.0.3 rio_0.5.16 rstudioapi_0.12 lsa_0.73.2 foreign_0.8-80 gridExtra_2.3 farver_2.0.3 Rtsne_0.15 digest_0.6.27 BiocManager_1.30.10 shiny_1.5.0 Rcpp_1.0.5 GenomicRanges_1.40.0 car_3.0-10 broom_0.7.2 later_1.1.0.1 RcppAnnoy_0.0.16 OrganismDbi_1.30.0 httr_1.4.2 AnnotationDbi_1.50.3 ggbio_1.36.0 biovizBase_1.36.0 colorspace_2.0-0 rvest_0.3.6 XML_3.99-0.3 fs_1.5.0 tensor_1.5 reticulate_1.18 IRanges_2.22.1 splines_4.0.3 RBGL_1.64.0 uwot_0.1.8.9001 RcppRoll_0.3.0 spatstat.utils_2.1-0 plotly_4.9.2.1 xtable_1.8-4
## [169] jsonlite_1.7.1 spatstat_1.64-1 R6_2.5.0 Hmisc_4.4-1 pillar_1.4.6 htmltools_0.5.1.1 mime_0.9 glue_1.4.2 fastmap_1.0.1 BiocParallel_1.22.0 codetools_0.2-17 lattice_0.20-41 curl_4.3 leiden_0.3.5 zip_2.1.1 openxlsx_4.2.3 openssl_1.4.3 survival_3.2-7 rmarkdown_2.5 munsell_0.5.0 GenomeInfoDbData_1.2.3 haven_2.3.1 gtable_0.3.0